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2019 | OriginalPaper | Buchkapitel

6. Ionospheric Space Weather Forecasting and Modelling

verfasst von : Ljiljana R. Cander

Erschienen in: Ionospheric Space Weather

Verlag: Springer International Publishing

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Abstract

Ionospheric weather prediction, specification, forecasting and modelling techniques that enable the realization of effective space weather products are described. In the future these may eventually be adopted and implemented by decision-making authorities for space environment specifications, warnings, and forecasts, all of which need to be timely, accurate, and reliable.

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Literatur
Zurück zum Zitat Cander LR, Stanković S, Milosavljević M (1998) Dynamic ionospheric prediction by neural networks. In: AI applications in solar-terrestrial physics proceedings, ESA WPP-148:225–228 Cander LR, Stanković S, Milosavljević M (1998) Dynamic ionospheric prediction by neural networks. In: AI applications in solar-terrestrial physics proceedings, ESA WPP-148:225–228
Zurück zum Zitat Cander LR, Milosavljević M, Tomašević S (2003a) Ionospheric storm forecasting technique by artificial neural network. Annals of Geofis 46(4):719–724 Cander LR, Milosavljević M, Tomašević S (2003a) Ionospheric storm forecasting technique by artificial neural network. Annals of Geofis 46(4):719–724
Zurück zum Zitat Cander LR, Bamford RA, Hickford JG (2003b) Nowcasting and forecasting the foF2, MUF(3000)F2 and TEC based on empirical models and real-time data. IEE Conference Proceedings 491(1):139–142 Cander LR, Bamford RA, Hickford JG (2003b) Nowcasting and forecasting the foF2, MUF(3000)F2 and TEC based on empirical models and real-time data. IEE Conference Proceedings 491(1):139–142
Zurück zum Zitat Cander LR (2015) Forecasting foF2 and MUF (3000) F2 ionospheric characteristics-a challenging space weather frontier. Adv Space Res 56:1973–1981CrossRef Cander LR (2015) Forecasting foF2 and MUF (3000) F2 ionospheric characteristics-a challenging space weather frontier. Adv Space Res 56:1973–1981CrossRef
Zurück zum Zitat Fausett L (1994) Fundamentals of neural networks. Prentice-Hall, Upper Saddle River, NJ Fausett L (1994) Fundamentals of neural networks. Prentice-Hall, Upper Saddle River, NJ
Zurück zum Zitat Haykin S (1994) Neural networks—a comprehensive foundation. Macmillan College Publishing Company, New York Haykin S (1994) Neural networks—a comprehensive foundation. Macmillan College Publishing Company, New York
Zurück zum Zitat ITU-R (1997) Recommendations P Series-Part 1. International Telecommunications Union, Geneva ITU-R (1997) Recommendations P Series-Part 1. International Telecommunications Union, Geneva
Zurück zum Zitat Kersley L, Malan D, Pryse ES et al (2004) Total electron content—a key parameter in propagation: measurement and use in ionospheric imaging. Ann Geofis 47:1067–1091 Kersley L, Malan D, Pryse ES et al (2004) Total electron content—a key parameter in propagation: measurement and use in ionospheric imaging. Ann Geofis 47:1067–1091
Zurück zum Zitat Lamming X, LR Cander (1999) Monthly median foF2 modelling COST251 area by neural networks. Phys Chem Earth 24:349–354 Lamming X, LR Cander (1999) Monthly median foF2 modelling COST251 area by neural networks. Phys Chem Earth 24:349–354
Zurück zum Zitat Levi MF, LR Cander, Dick MI et al (1999) Real-time ionospheric forecasting. IRI News 6:1–5 Levi MF, LR Cander, Dick MI et al (1999) Real-time ionospheric forecasting. IRI News 6:1–5
Zurück zum Zitat Mir Reza GR, Voosoghi B (2016) Wavelet neural networks using particle swarm optimization training in modeling regional ionospheric total electron content. J Atmos Sol-Terr Phys 149:21–30CrossRef Mir Reza GR, Voosoghi B (2016) Wavelet neural networks using particle swarm optimization training in modeling regional ionospheric total electron content. J Atmos Sol-Terr Phys 149:21–30CrossRef
Zurück zum Zitat Muhtarov P, Kutiev I (1999) Autocorrelation method for temporal interpolation and short-term prediction of ionospheric data. Radio Sci 34:459–464CrossRef Muhtarov P, Kutiev I (1999) Autocorrelation method for temporal interpolation and short-term prediction of ionospheric data. Radio Sci 34:459–464CrossRef
Zurück zum Zitat Oliver MA, Webster R (1990) Kriging: a method of interpolation for geographical information systems. Int J Geograph Info Sys 4:313–332 Oliver MA, Webster R (1990) Kriging: a method of interpolation for geographical information systems. Int J Geograph Info Sys 4:313–332
Zurück zum Zitat Piggott WR, Rawer K (1972a) U.R.S.I. Handbook of ionogram interpretation and reduction. Report UAG-23. National Oceanic and Atmospheric Administration, Boulder Piggott WR, Rawer K (1972a) U.R.S.I. Handbook of ionogram interpretation and reduction. Report UAG-23. National Oceanic and Atmospheric Administration, Boulder
Zurück zum Zitat Piggott WR, Rawer K (1972b) U.R.S.I. Handbook of ionogram interpretation and reduction. Report UAG-23A. Second Edition, Revision of Chaps. 1–4. National Oceanic and Atmospheric Administration, Boulder Piggott WR, Rawer K (1972b) U.R.S.I. Handbook of ionogram interpretation and reduction. Report UAG-23A. Second Edition, Revision of Chaps. 1–4. National Oceanic and Atmospheric Administration, Boulder
Zurück zum Zitat Pezzopane M, Pietrella M, Pignatelli A et al (2011) Assimilation of autoscaled data and regional and local ionospheric models as input sources for real-time 3-D International Reference Ionosphere modeling. Radio Sci 46 RS5009. https://doi.org/10.1029/rs004697 Pezzopane M, Pietrella M, Pignatelli A et al (2011) Assimilation of autoscaled data and regional and local ionospheric models as input sources for real-time 3-D International Reference Ionosphere modeling. Radio Sci 46 RS5009. https://​doi.​org/​10.​1029/​rs004697
Zurück zum Zitat Pezzopane M, Pietrella M, Pignatelli A et al (2013) Testing the three-dimensional IRI-SIRMUP-P mapping of the ionosphere for disturbed periods. Adv Space Res 52:1726–1736CrossRef Pezzopane M, Pietrella M, Pignatelli A et al (2013) Testing the three-dimensional IRI-SIRMUP-P mapping of the ionosphere for disturbed periods. Adv Space Res 52:1726–1736CrossRef
Zurück zum Zitat Poole AWV, McKinnell LA (1998) Short term prediction of foF2 using neural networks. WDC Report UAG-105, pp 109–111 Poole AWV, McKinnell LA (1998) Short term prediction of foF2 using neural networks. WDC Report UAG-105, pp 109–111
Zurück zum Zitat Radicella SM (2010) The NeQuick model genesis, uses and evolution. Ann Geofis 52:239–243 Radicella SM (2010) The NeQuick model genesis, uses and evolution. Ann Geofis 52:239–243
Zurück zum Zitat Tulunay E, Ozkaptan C, Tulunay Y (2000) Temporal and spatial forecasting of the foF2 values up to twenty four hours in advance. Phys Chem Earth 25:281–285CrossRef Tulunay E, Ozkaptan C, Tulunay Y (2000) Temporal and spatial forecasting of the foF2 values up to twenty four hours in advance. Phys Chem Earth 25:281–285CrossRef
Zurück zum Zitat Tulunay E, Senalp ET, LR Cander et al (2004) Development of algorithms and software for forecasting, nowcasting and variability of TEC. Ann Geofis 47:1201–1214 Tulunay E, Senalp ET, LR Cander et al (2004) Development of algorithms and software for forecasting, nowcasting and variability of TEC. Ann Geofis 47:1201–1214
Zurück zum Zitat Vapnik V (1999) The nature of statistical learning theory. Springer, New York Vapnik V (1999) The nature of statistical learning theory. Springer, New York
Zurück zum Zitat Wintoft P, LR Cander (2000) Twenty-four hour predictions of foF2 using time delay neural networks. Radio Sci 35(2):395–408 Wintoft P, LR Cander (2000) Twenty-four hour predictions of foF2 using time delay neural networks. Radio Sci 35(2):395–408
Metadaten
Titel
Ionospheric Space Weather Forecasting and Modelling
verfasst von
Ljiljana R. Cander
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-319-99331-7_6